The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Oil and gas facilities, such as wells, well support apparatus, transmission systems and the like, fundamentally support civilized society by providing sources of efficient consumable energy. However, if these facilities are not constructed, maintained or operated according to recognized practices pertaining to safety, operations and maintenance, accidents or failures can occur with serious consequences to people and the environment. Therefore, the oil and gas industry has developed a need to evaluate and compare the performance of oil and gas facilities to various guidelines for safety and operation.
To date, determining whether such facilities are safe typically has involved human inspection and reporting, both by industry and by government. A lack of industry self-governance is expected to cause government to intrude with new regulations based upon guidelines or metrics with which industry may disagree. The development of improved industry self-monitoring, according to industry-accepted guidelines, is viewed as a beneficial way to reduce the intrusion of government into the industry.
In other fields, computer-based scoring systems have been used. For example, in the field of consumer finance, the FICO score is a recognized metric for comparative measurement of creditworthiness. Based upon input values such as consumer income, consumer debt, number of debt items, and geographic location, the FICO score presents a consolidated view of lending risk associated with the consumer.
In light of recent highly publicized incidents involving energy production facilities such as oil and gas wells, stakeholders have expressed a desire to better understand the technically complicated and often obscure risks that oil and gas operators are taking as they extract our natural resources. However, condensing a vast number of data fields into a single, representative score is technically challenging and has only been accomplished in a few ways. By far, the most common method of assessment is using a manual checklist that explicitly calls out discrete items which must accomplished. This method is simple, objective and intuitive. An assessor, who may be an end user of the computer systems described herein, can guarantee that bare minimums are met and be confident in what the assessment represents. In the oil and gas field, this is the approach taken by organizations like Equitable Origins and Center for Sustainable Shale Development. Outside the oil and gas industry, the USGBC has improved on a simple check list by adding different weighting values in their LEED rating. This method adds a bit more intelligence to a simple check list and allows the most important elements to carry more weight; however the check list method is inherently limited in several ways.
First, it is inflexible because it explicitly requires specific components without considering the relative exposure. “Exposure,” in this context, refers to the likelihood of occurrence of an incident, such as the risk of a safety violation or apparatus failure resulting in damage to persons, property or environment. This inflexible approach results in a weak, bare minimum rating, which cannot consider the nuances of different areas or an overly harsh or conservative rating which is not normalized across different exposures. Second, the method cannot consider the relative importance of combinatorics problems. For example, in a set of five options, the first, second and third options together may be worth the same as option four alone. Neither a relative ranking scheme nor flat averages can account for these relative relations. Third, this method cannot appropriately account for uncertainty. If an answer is unknown it must be checked “No.” Users of check lists, like the USGBC, have simply given a 0 for each item which cannot be verified, encouraging their clients to provide information, but this approach can severely limit the usefulness of an assessment and is not practical when uncertainty stems from more than just document availability. Fourth, the checklist method must make a cost benefit trade off and decide on the “right” way to do things. In reality, there are likely a number of equivalent ways to do things; such nuances are not captured. Additionally the best performers are not given recognition for going above and beyond this standard, reducing incentive to do so. This is a problem that regulatory rule makers face alongside those groups creating standards. Finally, none of the checklist methods purport to offer an estimate of “expected impact.” They are neither comprehensive nor analytical enough to do so.
In the end, checklists represent a version of reality but are severely limited due to the above shortcomings.
In finance, FICO has capitalized on the abundance of information to generate a scoring algorithm that does a better job of representing reality but it is heavily dependent on vast amounts of data. The FICO score algorithm does not solve the four problems listed it above; it avoids the first, third and fourth by leveraging the abundance of data and resulting correlations, and uses a relative weighting scheme to address the second, which is largely sufficient because all the parts are statistically related.
On balance, prior approaches consist of: Process measurements, which are converted to a score via a checklist and weighting, and data measurements that are converted to a score via decision tree+algorithm. The process measurement approach is challenged by limited data and has a complicated link between process and performance. It is subject to the five issues summarized above, and often represents a single option rather than a continuum of choices. The data measurement approach is dependent on obtaining good quality data; it provides a less constrained environment and no uncertainty, flexibility or exposure concerns because data is normalized at top level already.